PAI-Rec Engine Diagnosis and Configuration Validation#

This skill provides comprehensive diagnostic and validation capabilities for Alibaba Cloud PAI-Rec (Programmable Recommendation System) engines, including interface troubleshooting and configuration analysis.

Scenario Description#

PAI-Rec is Alibaba Cloud's programmable recommendation system that provides intelligent recommendation capabilities. This skill helps users:

1. Diagnose PAI-Rec Engine Interface Issues: When engine API returns errors or unexpected results, trace the request through EAS service logs and engine configurations to identify root causes.

2. Validate Engine Configurations: Analyze engine configuration files for potential issues, inconsistencies, or misconfigurations before deployment.

Architecture: PAI-EAS Service + PAI-Rec Engine + Engine Configuration Management

Key Components#

• PAI-EAS Service: Elastic Algorithm Service hosting the recommendation engine
• PAI-Rec Engine: The recommendation engine processing requests
• Engine Configuration: Configuration files defining engine behavior
• Service Logs: EAS service logs containing request traces

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Installation#

Pre-check: Aliyun CLI >= 3.3.3 required

Run aliyun version to verify >= 3.3.3. If not installed or version too low, run curl -fsSL https://aliyuncli.alicdn.com/setup.sh | bash to install/update, or see references/cli-installation-guide.md for installation instructions.

Pre-check: Aliyun CLI plugin update required

[MUST] run aliyun configure set --auto-plugin-install true to enable automatic plugin installation. [MUST] run aliyun plugin update to ensure that any existing plugins are always up-to-date.

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Authentication#

Pre-check: Alibaba Cloud Credentials Required

Security Rules:

• NEVER read, echo, or print AK/SK values (e.g., echo $ALIBABA_CLOUD_ACCESS_KEY_ID is FORBIDDEN)
• NEVER ask the user to input AK/SK directly in the conversation or command line
• NEVER use aliyun configure set with literal credential values
• ONLY use aliyun configure list to check credential status

bash

aliyun configure list

Check the output for a valid profile (AK, STS, or OAuth identity).

If no valid profile exists, STOP here.

1. Obtain credentials from Alibaba Cloud Console
2. Configure credentials outside of this session (via aliyun configure in terminal or environment variables in shell profile)
3. Return and re-run after aliyun configure list shows a valid profile

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RAM Policy#

[MUST] Permission Failure Handling: When any command or API call fails due to permission errors at any point during execution, follow this process:

1. Read references/ram-policies.md to get the full list of permissions required by this SKILL
2. Use ram-permission-diagnose skill to guide the user through requesting the necessary permissions
3. Pause and wait until the user confirms that the required permissions have been granted

For detailed RAM policies required by this skill, see references/ram-policies.md.

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Parameter Confirmation#

IMPORTANT: Parameter Confirmation — Before executing any command or API call, ALL user-customizable parameters (e.g., RegionId, service names, instance IDs, environment names, config names, etc.) MUST be confirmed with the user. Do NOT assume or use default values without explicit user approval.

Required Parameters#

Parameter NameRequired/OptionalDescriptionDefault Valueservice-nameRequiredPAI-EAS service nameN/Acluster-idRequiredEAS cluster/region IDN/Ainstance-idRequiredPAI-Rec instance IDN/Arequest-idRequired (for diagnosis)Request ID from engine API responseN/Aconfig-nameOptionalEngine configuration nameN/AenvironmentOptionalEngine environment (Prod/Pre)N/Aengine-config-idOptionalSpecific engine config version IDN/AregionOptionalAlibaba Cloud regionN/A

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Observability (MUST follow for every aliyun command)#

Upon loading this skill, generate a random session ID (32-char lowercase hex string) once for the entire session. Use it as {session-id} below.

Rule: Every aliyun CLI command that calls a cloud API MUST include the --user-agent flag. Local utility commands (e.g. configure, plugin, version) do not support this flag and should be excluded.

text

--user-agent AlibabaCloud-Agent-Skills/alibabacloud-pai-rec-diagnosis/{session-id}

Example (assuming session-id is a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6):

bash

aliyun eas describe-service --cluster-id cn-hangzhou --service-name my_service --user-agent AlibabaCloud-Agent-Skills/alibabacloud-pai-rec-diagnosis/a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6

Do not skip, alter the format, or omit --user-agent on any aliyun API command invocation.

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Core Workflow#

[MUST] Allocate a per-session work directory — All transient artifacts (raw config dumps, log dumps, sanitized output) MUST be written under a unique per-session directory to avoid concurrent overwrite between parallel skill invocations. Run the following at the start of the workflow, before any artifact-producing CLI invocation, and reuse $WORKDIR for the whole session. Pass the full template as a positional arg (works on both BSD/macOS and GNU/Linux mktemp); do NOT use -t prefix, which falls back to $TMPDIR (e.g. /var/folders on macOS) and may even fail under sandboxed shells.

bash

export WORKDIR=$(mktemp -d /tmp/pairec-diag-XXXXXX)

All file paths shown below ( $WORKDIR/engine_configs_list.json, etc.) live inside this directory and MUST NOT be replaced with hard-coded /tmp/... paths.

[MUST] Use ONLY the workflows defined below. Do NOT invent additional steps (e.g. instance resource checks, network probes) or substitute manual analysis for a defined workflow step.

Workflow 1: PAI-Rec Engine Interface Diagnosis#

This workflow helps diagnose issues when a PAI-Rec engine API returns errors or unexpected results.

Input Example:

text

Service Name: embedding_recall
API Response:
{
    "code": 299,
    "msg": "items size not enough",
    "request_id": "941b4e14-d1c5-489f-a184-b2b17f8b4fdb",
    "size": 0,
    "experiment_id": "",
    "items": []
}

Step 1: Retrieve EAS Service Information

Get the service details to find the EAS service ID and configuration:

bash

aliyun eas describe-service \
  --cluster-id <cluster-id> \
  --service-name <service-name>

What to extract:

• Resource: EAS service resource ID (e.g., eas-r-1v4qb1yan3qmnjwxqe)

• ServiceConfig.envs: Environment variables containing:

  • REGION: The region
  • INSTANCE_ID: PAI-Rec instance ID
  • CONFIG_NAME: Engine configuration name
  • PAIREC_ENVIRONMENT: Environment (product/prepub)

Step 2: Extract Request ID from API Response

Parse the API response JSON to get the request_id field. This will be used to search service logs.

Step 3: Query EAS Service Logs

Use the request ID as the sole filter to search service logs. Do NOT pass --start-time / --end-time when searching PAI-Rec business logs:

bash

aliyun eas describe-service-log \
  --cluster-id <cluster-id> \
  --service-name <service-name> \
  --keyword <request-id> \
  --page-size 500

[CRITICAL] --keyword <request-id> is MANDATORY — local post-processing is FORBIDDEN:

• You MUST pass --keyword <request-id> (server-side filter, exact case-sensitive match on the full request_id). The API returns only log lines matching the keyword.
• You MUST NOT omit --keyword and then filter locally (e.g., piping through head, grep, python3, jq, or any script).
• You MUST NOT make multiple describe-service-log calls without --keyword hoping to find relevant lines by scanning the full log stream.
• If a call with --keyword returns empty results, report that no matching logs were found — do NOT fall back to fetching unfiltered logs.
• --page-size 500 captures the entire trace in a single page; matched entries for one request are usually < 30.

❌ WRONG (fetches ALL logs, filters locally — FORBIDDEN):

bash

aliyun eas describe-service-log --cluster-id cn-beijing --service-name embedding_recall | head -300
aliyun eas describe-service-log --cluster-id cn-beijing --service-name embedding_recall | grep "request_id"

✅ RIGHT (server-side keyword filter — REQUIRED):

bash

aliyun eas describe-service-log --cluster-id cn-beijing --service-name embedding_recall --keyword 0c6cbd91-5618-4705-8e08-9126bf4600f7 --page-size 500

[CRITICAL] Time range silently drops business logs:

• With --keyword only (no time range), the CLI returns the full PAI-Rec application trace ( controller.go / feed.go / recall.go / rank_service.go etc.) matching the request_id.
• Adding --start-time / --end-time — even when the window covers the real timestamp — silently drops business logs and returns only infrastructure noise ( /bin/sh heartbeats, 502 Bad Gateway retries, postgres.go dbstat).
• Use time ranges only for broad scans WITHOUT --keyword, in yyyy-MM-dd HH:mm:ss UTC format (no T / no Z); ISO-8601 forms like 2025-04-28T00:00:00Z are rejected with InvalidParameter.

Step 4: List Engine Configurations

Map the environment and list matching configurations:

Environment Mapping:

• product → Prod
• prepub → Pre

bash

aliyun pairecservice list-engine-configs \
  --instance-id <instance-id> \
  --environment <Prod|Pre> \
  --status Released \
  --name <config-name> > "$WORKDIR/engine_configs_list.json" 2>&1

[MUST] Always pass --name <config-name> for server-side filtering: <config-name> is already known from Step 1 ( ServiceConfig.envs.CONFIG_NAME); forward it as --name. Omitting it returns the entire instance's config inventory (often hundreds of unrelated entries), forces client-side filtering, wastes tokens, and risks hitting CLI default pagination so the target row is silently dropped. --name is an exact-match filter on the server; do NOT substitute with grep / jq select post-processing. The same rule applies to every list-engine-configs invocation in this skill (Workflow 2 Step 1 included).

What to extract:

• Find the configuration with Status: Released
• Get EngineConfigId and Version

Step 5: Get Engine Configuration Details

bash

aliyun pairecservice get-engine-config \
  --instance-id <instance-id> \
  --engine-config-id <engine-config-id> > "$WORKDIR/raw_engine_config.json" 2>&1

[MUST] Sanitize before display — Config may contain plaintext passwords or access keys. Always pipe through the sanitizer before printing to terminal; only sanitized output (with credentials replaced by ***REDACTED***) should appear there. The raw file at $WORKDIR/raw_engine_config.json can be passed directly to scripts/validate.py (which does not print credential values).

bash

python3 scripts/sanitize_config.py "$WORKDIR/raw_engine_config.json"

What to extract:

• ConfigValue: The actual engine configuration (JSON/YAML)

Step 5.5 (Optional): Static Config Sanity Check

Run scripts/validate.py against the retrieved ConfigValue to rule out structural / reference errors. See references/config-validation.md.

bash

printf '%s' "$CONFIG_VALUE" | python3 scripts/validate.py --stdin

When to run: when logs point at a config element or when diagnosing the config for the first time. When to skip: when logs show a non-config root cause (missing scene_id, upstream 5xx). [MUST NOT] Do not replace or duplicate validate.py (same restriction as Workflow 2 § Step 3). [MUST] Scoping rule: findings enter the final diagnosis ONLY when tied to log evidence for the current request_id.

Step 5a (Conditional): Retrieve Experiment Configuration

Condition: experiment_id in the API response is non-empty (e.g., "ER14_L21_L26#EG21_L38#EG38#E44_GL36_GL37").

Parse: Extract EG{id} (experiment group) and E{id} (experiment) numeric IDs from the string. Ignore ER, L, GL prefixes — they carry no config.

bash

# For each EG{id}:
aliyun pairecservice get-experiment-group \
  --instance-id <instance-id> \
  --experiment-group-id <id> > "$WORKDIR/experiment_group_<id>.json" 2>&1
# For each E{id}:
aliyun pairecservice get-experiment \
  --instance-id <instance-id> \
  --experiment-id <id> > "$WORKDIR/experiment_<id>.json" 2>&1

What to extract: The Config field — contains override parameters (e.g., default.RecallNames, rankconf, filterNames, default.SortNames) that supersede the base engine config.

Override priority (low → high): Base Engine Config < ExperimentGroup.Config < Experiment.Config. Apply in Step 6 to understand actual runtime behavior.

Validate experiment configs against the base config (reference existence check):

bash

python3 scripts/validate.py "$WORKDIR/raw_engine_config.json" \
  --experiment-config "$WORKDIR/experiment_group_<id>.json" \
  --experiment-config "$WORKDIR/experiment_<id>.json"

Step 6: Comprehensive Analysis

Analyze the following components together:

1. API Response: Error code, message, and returned data
2. Service Logs: Trace logs for the request_id showing processing flow
3. Engine Configuration: Settings that may affect the behavior
4. Experiment Overrides (if experiment_id non-empty): Effective config = base config with experiment parameters applied on top

Common Issues to Check:

• Configuration mismatches (e.g., recall settings, filtering rules)
• Experiment overrides (e.g., experiment changed RecallNames / rankconf from base config)
• Resource limitations (e.g., insufficient items, timeout settings)
• Data source issues (e.g., table access, feature availability)
• Environment inconsistencies (e.g., prod config in prepub environment)

[MUST] Evidence-only reporting rule:

The final diagnosis delivered to the user MUST be grounded strictly in what the EAS service logs and the engine configuration directly show. Apply the following constraints:

• Report only what is observed. Quote the exact log line (file:line, level, message) and the exact config fragment that proves each claim.

• State the direct causal chain from log evidence to the API response, and stop there.

• Do NOT add any of the following unless the user explicitly asks:

  • Speculative root causes not visible in logs/config (e.g., "client probably sent wrong X")
  • Fix recommendations or remediation steps
  • Conditional "if X then Y" scenarios
  • Tangential best-practice advice (security, fallback design, naming, etc.)
  • Guesses about upstream systems, client code, or data sources not covered by the logs/config

• If the evidence is insufficient to reach a conclusion, state explicitly what additional data (specific log lines, other config versions, other environments) is needed, instead of guessing.

• Recommendations are opt-in only. Provide fixes/suggestions only when the user explicitly requests them in a follow-up.

---

Workflow 2: PAI-Rec Engine Configuration Validation#

This workflow validates engine configurations for potential issues.

Input: Configuration name and environment (Prod/Pre)

Step 1: List Configuration Versions

If user doesn't provide engine-config-id, list available versions:

bash

aliyun pairecservice list-engine-configs \
  --instance-id <instance-id> \
  --environment <Prod|Pre> \
  --name <config-name>

Display to user:

• Version: Version number
• Status: Configuration status (Released/Draft/Archived)
• GmtCreateTime: Creation timestamp
• EngineConfigId: Version ID

Ask user to select a version or provide the engine-config-id.

Step 2: Retrieve Configuration Details

bash

aliyun pairecservice get-engine-config \
  --instance-id <instance-id> \
  --engine-config-id <engine-config-id> > "$WORKDIR/raw_engine_config.json" 2>&1

[MUST] Sanitize before display — Always sanitize before printing to terminal:

bash

python3 scripts/sanitize_config.py "$WORKDIR/raw_engine_config.json"

Step 3: Run Schema + Rule Validation

[MUST] Feed the extracted ConfigValue JSON into scripts/validate.py. The script enforces JSON Schema ( references/schema.json) + reference-consistency rules and exits with status 0 on pass, 1 on failure.

bash

# From a saved JSON file (recommended)
python3 scripts/validate.py "$WORKDIR/raw_engine_config.json"
# Or pipe ConfigValue directly via stdin
printf '%s' "$CONFIG_VALUE" | python3 scripts/validate.py --stdin

Requires jsonschema ( pip install jsonschema); if missing the script falls back to rule-only validation without Schema checks.

[MUST NOT] Do not replace or duplicate validate.py:

• Do NOT skip it; do NOT hand-write equivalent checks in Python / jq / grep / any other tool — the script is the authoritative validator.
• Do NOT re-implement, re-check, or "double-confirm" any rule after the script has run; trust its output verbatim, including a clean 0 error(s), 0 warning(s) run.
• If the script cannot run (missing Python, dependency issue, etc.), fix the environment and re-run — do NOT fall back to manual checking.
• Inspections OUTSIDE the script's scope are still allowed (see Step 4).

What the script checks (summary):

1. Structure — JSON well-formedness, required fields, types ( RecallConfs, FilterConfs, SortConfs, AlgoConfs, SceneConfs, RankConf, FeatureConfs, UserFeatureConfs, DebugConfs, FeatureLogConfs, CallBackConfs, PipelineConfs, etc.)
2. Enum values — RecallType / FilterType / SortType / DebugConfs.OutputType / GeneralRankConfs.ActionConfs[].ActionType
3. Reference consistency — SceneConfs.RecallNames → RecallConfs; FilterNames → FilterConfs; SortNames → SortConfs; RankConf.RankAlgoList → AlgoConfs; any DaoConf.AdapterType + *Name → the corresponding *Confs (Hologres / Redis / MySQL / TableStore / FeatureStore / …)
4. Business rules
   • User2ItemExposureFilter with WriteLog=true + FeatureStore adapter: must set TimeInterval > 0
   • PriorityAdjustCountFilter in accumulator mode: Count must be strictly increasing (use Type="fix" for independent per-recall caps)
   • PipelineConfs.*.Name must be globally unique
   • DebugConfs.Rate must be an integer in [0, 100]
5. Duplicate name detection within RecallConfs, FilterConfs, SortConfs, AlgoConfs

Detailed usage, exit codes, example outputs and the full rule list live in references/config-validation.md.

Step 4: Evidence-Grounded Report

[MUST] Required first line of the report: Quote validate.py's stdout verbatim — either Validation passed: configuration is well-formed or Validation finished: N error(s), M warning(s). A report missing this exact line is INVALID; restart from Step 3.

Manual inspection is allowed only for concerns out of scope of validate.py: env / region / model-signature mismatches, cross-version diffs, naming collisions between RankScore variables and model output fields, and root-cause reading of any [WARNING] the script itself asks a human to judge. Do NOT add findings the script did not report unless you can tie them to one of these out-of-scope concerns.

Report structure:

• ✅ Checks passed — quote validate.py's 0 error(s), 0 warning(s) line
• ⚠️ Warnings — copy each [WARNING] <path>: <message> from the script, plus any out-of-scope inconsistency from manual inspection
• ❌ Errors — copy each [ERROR] <path>: <message> from the script
• Missing-evidence notes — ONLY when ≥1 ⚠️ warning is listed: state what extra data would upgrade that warning into a confirmed error. With 0 warnings, OMIT this section; do NOT fill it with generic out-of-scope disclaimers (cross-version diff, remote connectivity, region/endpoint consistency) — those are volunteered best-practice advice forbidden by the evidence-only rule.

Do not add speculative fixes or best-practice tangents; suggestions are provided only when the user explicitly asks for them.

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Success Verification Method#

For detailed verification steps, see references/verification-method.md.

Quick Verification:

1. For Diagnosis Workflow:
   • Service information retrieved successfully
   • Logs found containing the request_id
   • Configuration loaded correctly
   • Root cause identified
2. For Validation Workflow:
   • Configuration retrieved successfully
   • All validation checks executed
   • Issues clearly reported
   • Recommendations provided (if applicable)

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Cleanup#

This skill performs read-only Alibaba Cloud API calls (no remote resources are created). Transient artifacts go to a per-session local $WORKDIR under /tmp (see Core Workflow preamble). The skill does NOT delete $WORKDIR automatically — the OS-level temp policy reclaims it (macOS reaps /tmp periodically; most Linux distros reap on reboot or via systemd-tmpfiles). To free disk space sooner, manually run rm -rf /tmp/pairec-diag-* outside the workflow.

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Best Practices#

1. Log queries — keyword only, no time range, no local filtering: For request-level diagnosis, pass --keyword <request_id> to aliyun eas describe-service-log and leave --start-time / --end-time unset. NEVER omit --keyword and post-process locally (e.g., | head, | grep, | python3) — this defeats server-side filtering, wastes tokens, and may miss logs beyond the first page. Combining keyword with a time range filters out business logs due to a CLI quirk (see Workflow 1, Step 3). Only use time ranges for broad non-request scans, and only with the yyyy-MM-dd HH:mm:ss UTC format (no T / no Z).
2. Trust validate.py: For Workflow 2, treat scripts/validate.py as the single source of truth for the rules in its catalogue. Do NOT skip it and hand-write checks, and do NOT re-validate its rules manually after a clean run. Manual inspection is reserved for concerns out of its scope (env / region / model signature, cross-version diffs, RankScore vs model output naming).
3. Environment awareness: Always verify that configurations match the target environment (Prod vs Pre); compare against a known-good version when issues persist.
4. Log retention: EAS service logs are retained for limited periods; diagnose issues promptly after occurrence.
5. Evidence-only conclusions: Ground every statement on a specific log line or config fragment. Follow the systematic workflow rather than jumping to conclusions from error messages alone. Do not speculate, do not propose fixes, and do not volunteer best-practice advice unless the user explicitly asks. If the evidence is insufficient, say what is missing rather than inferring.

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Reference Links#

Reference DocumentDescriptionRAM PoliciesRequired RAM permissions for PAI-Rec and EAS APIsRelated CommandsComplete CLI command referenceVerification MethodDetailed verification proceduresCLI Installation GuideAlibaba Cloud CLI installation instructionsConfiguration ExamplesSample engine configurations and common patternsConfig Validationscripts/validate.py usage, exit codes, rule catalogueTroubleshooting GuideCommon issues and solutionsConfig SanitizationCredential redaction before LLM analysis